19,635 research outputs found
Internet of robotic things : converging sensing/actuating, hypoconnectivity, artificial intelligence and IoT Platforms
The Internet of Things (IoT) concept is evolving rapidly and influencing newdevelopments in various application domains, such as the Internet of MobileThings (IoMT), Autonomous Internet of Things (A-IoT), Autonomous Systemof Things (ASoT), Internet of Autonomous Things (IoAT), Internetof Things Clouds (IoT-C) and the Internet of Robotic Things (IoRT) etc.that are progressing/advancing by using IoT technology. The IoT influencerepresents new development and deployment challenges in different areassuch as seamless platform integration, context based cognitive network integration,new mobile sensor/actuator network paradigms, things identification(addressing, naming in IoT) and dynamic things discoverability and manyothers. The IoRT represents new convergence challenges and their need to be addressed, in one side the programmability and the communication ofmultiple heterogeneous mobile/autonomous/robotic things for cooperating,their coordination, configuration, exchange of information, security, safetyand protection. Developments in IoT heterogeneous parallel processing/communication and dynamic systems based on parallelism and concurrencyrequire new ideas for integrating the intelligent âdevicesâ, collaborativerobots (COBOTS), into IoT applications. Dynamic maintainability, selfhealing,self-repair of resources, changing resource state, (re-) configurationand context based IoT systems for service implementation and integrationwith IoT network service composition are of paramount importance whennew âcognitive devicesâ are becoming active participants in IoT applications.This chapter aims to be an overview of the IoRT concept, technologies,architectures and applications and to provide a comprehensive coverage offuture challenges, developments and applications
Robotic ubiquitous cognitive ecology for smart homes
Robotic ecologies are networks of heterogeneous robotic devices pervasively embedded in everyday environments, where they cooperate to perform complex tasks. While their potential makes them increasingly popular, one fundamental problem is how to make them both autonomous and adaptive, so as to reduce the amount of preparation, pre-programming and human supervision that they require in real world applications. The project RUBICON develops learning solutions which yield cheaper, adaptive and efficient coordination of robotic ecologies. The approach we pursue builds upon a unique combination of methods from cognitive robotics, machine learning, planning and agent- based control, and wireless sensor networks. This paper illustrates the innovations advanced by RUBICON in each of these fronts before describing how the resulting techniques have been integrated and applied to a smart home scenario. The resulting system is able to provide useful services and pro-actively assist the users in their activities. RUBICON learns through an incremental and progressive approach driven by the feed- back received from its own activities and from the user, while also self-organizing the manner in which it uses available sensors, actuators and other functional components in the process. This paper summarises some of the lessons learned by adopting such an approach and outlines promising directions for future work
Microservices and Machine Learning Algorithms for Adaptive Green Buildings
In recent years, the use of services for Open Systems development has consolidated and strengthened. Advances in the Service Science and Engineering (SSE) community, promoted by the reinforcement of Web Services and Semantic Web technologies and the presence of new Cloud computing techniques, such as the proliferation of microservices solutions, have allowed software architects to experiment and develop new ways of building open and adaptable computer systems at runtime. Home automation, intelligent buildings, robotics, graphical user interfaces are some of the social atmosphere environments suitable in which to apply certain innovative trends. This paper presents a schema for the adaptation of Dynamic Computer Systems (DCS) using interdisciplinary techniques on model-driven engineering, service engineering and soft computing. The proposal manages an orchestrated microservices schema for adapting component-based software architectural systems at runtime. This schema has been developed as a three-layer adaptive transformation process that is supported on a rule-based decision-making service implemented by means of Machine Learning (ML) algorithms. The experimental development was implemented in the Solar Energy Research Center (CIESOL) applying the proposed microservices schema for adapting home architectural atmosphere systems on Green Buildings
Using the DiaSpec design language and compiler to develop robotics systems
A Sense/Compute/Control (SCC) application is one that interacts with the
physical environment. Such applications are pervasive in domains such as
building automation, assisted living, and autonomic computing. Developing an
SCC application is complex because: (1) the implementation must address both
the interaction with the environment and the application logic; (2) any
evolution in the environment must be reflected in the implementation of the
application; (3) correctness is essential, as effects on the physical
environment can have irreversible consequences. The SCC architectural pattern
and the DiaSpec domain-specific design language propose a framework to guide
the design of such applications. From a design description in DiaSpec, the
DiaSpec compiler is capable of generating a programming framework that guides
the developer in implementing the design and that provides runtime support. In
this paper, we report on an experiment using DiaSpec (both the design language
and compiler) to develop a standard robotics application. We discuss the
benefits and problems of using DiaSpec in a robotics setting and present some
changes that would make DiaSpec a better framework in this setting.Comment: DSLRob'11: Domain-Specific Languages and models for ROBotic systems
(2011
On the Integration of Adaptive and Interactive Robotic Smart Spaces
© 2015 Mauro Dragone et al.. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License. (CC BY-NC-ND 3.0)Enabling robots to seamlessly operate as part of smart spaces is an important and extended challenge for robotics R&D and a key enabler for a range of advanced robotic applications, such as AmbientAssisted Living (AAL) and home automation. The integration of these technologies is currently being pursued from two largely distinct view-points: On the one hand, people-centred initiatives focus on improving the userâs acceptance by tackling human-robot interaction (HRI) issues, often adopting a social robotic approach, and by giving to the designer and - in a limited degree â to the final user(s), control on personalization and product customisation features. On the other hand, technologically-driven initiatives are building impersonal but intelligent systems that are able to pro-actively and autonomously adapt their operations to fit changing requirements and evolving usersâ needs,but which largely ignore and do not leverage human-robot interaction and may thus lead to poor user experience and user acceptance. In order to inform the development of a new generation of smart robotic spaces, this paper analyses and compares different research strands with a view to proposing possible integrated solutions with both advanced HRI and online adaptation capabilities.Peer reviewe
mRUBiS: An Exemplar for Model-Based Architectural Self-Healing and Self-Optimization
Self-adaptive software systems are often structured into an adaptation engine
that manages an adaptable software by operating on a runtime model that
represents the architecture of the software (model-based architectural
self-adaptation). Despite the popularity of such approaches, existing exemplars
provide application programming interfaces but no runtime model to develop
adaptation engines. Consequently, there does not exist any exemplar that
supports developing, evaluating, and comparing model-based self-adaptation off
the shelf. Therefore, we present mRUBiS, an extensible exemplar for model-based
architectural self-healing and self-optimization. mRUBiS simulates the
adaptable software and therefore provides and maintains an architectural
runtime model of the software, which can be directly used by adaptation engines
to realize and perform self-adaptation. Particularly, mRUBiS supports injecting
issues into the model, which should be handled by self-adaptation, and
validating the model to assess the self-adaptation. Finally, mRUBiS allows
developers to explore variants of adaptation engines (e.g., event-driven
self-adaptation) and to evaluate the effectiveness, efficiency, and scalability
of the engines
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